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              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/quick_start.html">Quick Start</a></li>
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<p class="caption" role="heading"><span class="caption-text">User Guide</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/torch_tensorrt_explained.html">Torch-TensorRT Explained</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/dynamic_shapes.html">Dynamic shapes with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/saving_models.html">Saving models compiled with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/runtime.html">Deploying Torch-TensorRT Programs</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/using_dla.html">DLA</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/mixed_precision.html">Compile Mixed Precision models with Torch-TensorRT</a></li>
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<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html">Torch Compile Advanced Usage</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/vgg16_ptq.html">Deploy Quantized Models using Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/engine_caching_example.html">Engine Caching</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/engine_caching_bert_example.html">Engine Caching (BERT)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/refit_engine_example.html">Refitting Torch-TensorRT Programs with New Weights</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_export_cudagraphs.html">Torch Export with Cudagraphs</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/converter_overloading.html">Overloading Torch-TensorRT Converters with Custom Converters</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/auto_generate_converters.html">Automatically Generate a Converter for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/auto_generate_plugins.html">Automatically Generate a Plugin for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html">Mutable Torch TensorRT Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/weight_streaming_example.html">Weight Streaming</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/pre_allocated_output_example.html">Pre-allocated output buffer</a></li>
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<p class="caption" role="heading"><span class="caption-text">Dynamo Frontend</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../dynamo/torch_compile.html">TensorRT Backend for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../dynamo/dynamo_export.html">Compiling Exported Programs with Torch-TensorRT</a></li>
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<p class="caption" role="heading"><span class="caption-text">TorchScript Frontend</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../ts/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/ptq.html">Post Training Quantization (PTQ)</a></li>
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<p class="caption" role="heading"><span class="caption-text">FX Frontend</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../fx/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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<p class="caption" role="heading"><span class="caption-text">Model Zoo</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet with dynamic shapes using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html">Compiling BERT using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.html">Compiling Stable Diffusion model using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_compile_gpt2.html">Compiling GPT2 using the Torch-TensorRT <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> frontend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_export_gpt2.html">Compiling GPT2 using the dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_export_llama2.html">Compiling Llama2 using the dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_export_sam2.html">Compiling SAM2 using the dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_export_flux_dev.html">Compiling FLUX.1-dev model using the Torch-TensorRT dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/notebooks.html">Legacy notebooks</a></li>
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<p class="caption" role="heading"><span class="caption-text">Python API Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../py_api/ptq.html">torch_tensorrt.ts.ptq</a></li>
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<p class="caption" role="heading"><span class="caption-text">C++ API Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../_cpp_api/namespace_torch_tensorrt.html">Namespace torch_tensorrt</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../_cpp_api/namespace_torch_tensorrt__logging.html">Namespace torch_tensorrt::logging</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../_cpp_api/namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
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<p class="caption" role="heading"><span class="caption-text">CLI Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../cli/torchtrtc.html">torchtrtc</a></li>
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<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
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  <h1>Source code for torch_tensorrt.dynamo._refit</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span><span class="w"> </span><span class="nn">__future__</span><span class="w"> </span><span class="kn">import</span> <span class="n">annotations</span>

<span class="kn">import</span><span class="w"> </span><span class="nn">collections.abc</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">copy</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">logging</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Tuple</span>

<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">tensorrt</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">trt</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch.export</span><span class="w"> </span><span class="kn">import</span> <span class="n">ExportedProgram</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch.fx.experimental.proxy_tensor</span><span class="w"> </span><span class="kn">import</span> <span class="n">unset_fake_temporarily</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt._enums</span><span class="w"> </span><span class="kn">import</span> <span class="n">dtype</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt._Input</span><span class="w"> </span><span class="kn">import</span> <span class="n">Input</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo</span><span class="w"> </span><span class="kn">import</span> <span class="n">partitioning</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo._exporter</span><span class="w"> </span><span class="kn">import</span> <span class="n">inline_torch_modules</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo._settings</span><span class="w"> </span><span class="kn">import</span> <span class="n">CompilationSettings</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo.conversion._conversion</span><span class="w"> </span><span class="kn">import</span> <span class="n">infer_module_output_dtypes</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo.conversion._ConverterRegistry</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
    <span class="n">DYNAMO_CONVERTERS</span> <span class="k">as</span> <span class="n">CONVERTERS</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo.conversion._TRTInterpreter</span><span class="w"> </span><span class="kn">import</span> <span class="n">TRTInterpreter</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo.conversion.truncate_double</span><span class="w"> </span><span class="kn">import</span> <span class="n">repair_double_inputs</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo.lowering</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
    <span class="n">get_decompositions</span><span class="p">,</span>
    <span class="n">post_lowering</span><span class="p">,</span>
    <span class="n">pre_export_lowering</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo.runtime._PythonTorchTensorRTModule</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
    <span class="n">PythonTorchTensorRTModule</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo.runtime._TorchTensorRTModule</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
    <span class="n">ENGINE_IDX</span><span class="p">,</span>
    <span class="n">SERIALIZED_METADATA_IDX</span><span class="p">,</span>
    <span class="n">TorchTensorRTModule</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo.utils</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
    <span class="n">check_module_output</span><span class="p">,</span>
    <span class="n">get_model_device</span><span class="p">,</span>
    <span class="n">get_torch_inputs</span><span class="p">,</span>
    <span class="n">set_log_level</span><span class="p">,</span>
    <span class="n">to_torch_device</span><span class="p">,</span>
    <span class="n">to_torch_tensorrt_device</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.logging</span><span class="w"> </span><span class="kn">import</span> <span class="n">TRT_LOGGER</span>

<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>


<span class="k">def</span><span class="w"> </span><span class="nf">construct_refit_mapping</span><span class="p">(</span>
    <span class="n">module</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span>
    <span class="n">inputs</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Input</span><span class="p">],</span>
    <span class="n">settings</span><span class="p">:</span> <span class="n">CompilationSettings</span> <span class="o">=</span> <span class="n">CompilationSettings</span><span class="p">(),</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;Find out the weight mapping between weight in exported program and TensorRT engine</span>
<span class="sd">    Args:</span>
<span class="sd">        module: FX GraphModule to interpret</span>
<span class="sd">        inputs: Sequence of Tensors representing inputs to the module</span>
<span class="sd">        settings: Compilation settings</span>
<span class="sd">    Returns:</span>
<span class="sd">        Mapping from weight name in TensorRT to actual weight value in np.ndarray</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">MODULE_MAP</span> <span class="o">=</span> <span class="p">{</span>
        <span class="s2">&quot;SCALE&quot;</span><span class="p">:</span> <span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">IScaleLayer</span><span class="p">,</span> <span class="p">[(</span><span class="s2">&quot;scale&quot;</span><span class="p">,</span> <span class="s2">&quot;SCALE&quot;</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;shift&quot;</span><span class="p">,</span> <span class="s2">&quot;SHIFT&quot;</span><span class="p">)]),</span>
        <span class="s2">&quot;CONVOLUTION&quot;</span><span class="p">:</span> <span class="p">(</span>
            <span class="n">trt</span><span class="o">.</span><span class="n">IConvolutionLayer</span><span class="p">,</span>
            <span class="p">[(</span><span class="s2">&quot;kernel&quot;</span><span class="p">,</span> <span class="s2">&quot;KERNEL&quot;</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;bias&quot;</span><span class="p">,</span> <span class="s2">&quot;BIAS&quot;</span><span class="p">)],</span>
        <span class="p">),</span>
        <span class="s2">&quot;DECONVOLUTION&quot;</span><span class="p">:</span> <span class="p">(</span>
            <span class="n">trt</span><span class="o">.</span><span class="n">IDeconvolutionLayer</span><span class="p">,</span>
            <span class="p">[(</span><span class="s2">&quot;kernel&quot;</span><span class="p">,</span> <span class="s2">&quot;KERNEL&quot;</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;bias&quot;</span><span class="p">,</span> <span class="s2">&quot;BIAS&quot;</span><span class="p">)],</span>
        <span class="p">),</span>
        <span class="s2">&quot;CONSTANT&quot;</span><span class="p">:</span> <span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">IConstantLayer</span><span class="p">,</span> <span class="p">[(</span><span class="s2">&quot;weights&quot;</span><span class="p">,</span> <span class="s2">&quot;CONSTANT&quot;</span><span class="p">)]),</span>
    <span class="p">}</span>

    <span class="n">output_dtypes</span> <span class="o">=</span> <span class="n">infer_module_output_dtypes</span><span class="p">(</span>
        <span class="n">module</span><span class="p">,</span>
        <span class="n">truncate_double</span><span class="o">=</span><span class="n">settings</span><span class="o">.</span><span class="n">truncate_double</span><span class="p">,</span>
    <span class="p">)</span>

    <span class="c1"># Use Interpreter</span>
    <span class="n">weight_map</span> <span class="o">=</span> <span class="p">{}</span>
    <span class="n">interpreter</span> <span class="o">=</span> <span class="n">TRTInterpreter</span><span class="p">(</span>
        <span class="n">module</span><span class="p">,</span>
        <span class="n">inputs</span><span class="p">,</span>
        <span class="n">logger_level</span><span class="o">=</span><span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">Logger</span><span class="o">.</span><span class="n">VERBOSE</span> <span class="k">if</span> <span class="n">settings</span><span class="o">.</span><span class="n">debug</span> <span class="k">else</span> <span class="n">trt</span><span class="o">.</span><span class="n">Logger</span><span class="o">.</span><span class="n">WARNING</span><span class="p">),</span>
        <span class="n">output_dtypes</span><span class="o">=</span><span class="n">output_dtypes</span><span class="p">,</span>
        <span class="n">compilation_settings</span><span class="o">=</span><span class="n">settings</span><span class="p">,</span>
    <span class="p">)</span>
    <span class="n">interpreter</span><span class="o">.</span><span class="n">_construct_trt_network_def</span><span class="p">()</span>
    <span class="n">net</span> <span class="o">=</span> <span class="n">interpreter</span><span class="o">.</span><span class="n">ctx</span><span class="o">.</span><span class="n">net</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">num_layers</span><span class="p">):</span>
        <span class="n">layer</span> <span class="o">=</span> <span class="n">net</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
        <span class="n">layer_type</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">type</span><span class="o">.</span><span class="n">name</span>
        <span class="k">if</span> <span class="n">layer_type</span> <span class="ow">in</span> <span class="n">MODULE_MAP</span><span class="p">:</span>
            <span class="c1"># Cast the parent class to child class to access attributes</span>
            <span class="c1"># For example: ILayer does not have ILayer.kernel/ILayer.bias</span>
            <span class="c1"># So we cast it to IConvolutionLayer and access the attributes</span>
            <span class="n">layer</span><span class="o">.</span><span class="vm">__class__</span> <span class="o">=</span> <span class="n">MODULE_MAP</span><span class="p">[</span><span class="n">layer_type</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
            <span class="k">for</span> <span class="n">weight_type</span><span class="p">,</span> <span class="n">weight_name</span> <span class="ow">in</span> <span class="n">MODULE_MAP</span><span class="p">[</span><span class="n">layer_type</span><span class="p">][</span><span class="mi">1</span><span class="p">]:</span>
                <span class="n">weight</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="fm">__getattribute__</span><span class="p">(</span><span class="n">weight_type</span><span class="p">)</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
                <span class="n">weight_dtype</span> <span class="o">=</span> <span class="n">dtype</span><span class="o">.</span><span class="n">try_from</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">DataType</span><span class="p">)</span>
                <span class="n">weight_map</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">layer</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">weight_name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
                    <span class="n">weight</span><span class="p">,</span>
                    <span class="n">weight_dtype</span><span class="p">,</span>
                <span class="p">)</span>

    <span class="k">return</span> <span class="n">weight_map</span>


<span class="k">def</span><span class="w"> </span><span class="nf">construct_refit_mapping_from_weight_name_map</span><span class="p">(</span>
    <span class="n">weight_name_map</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span> <span class="n">state_dict</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Any</span><span class="p">]</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Any</span><span class="p">]:</span>
    <span class="n">engine_weight_map</span> <span class="o">=</span> <span class="p">{}</span>
    <span class="k">for</span> <span class="n">engine_weight_name</span><span class="p">,</span> <span class="p">(</span><span class="n">sd_weight_name</span><span class="p">,</span> <span class="n">np_weight_type</span><span class="p">)</span> <span class="ow">in</span> <span class="n">weight_name_map</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
        <span class="n">trt_dtype</span> <span class="o">=</span> <span class="n">dtype</span><span class="o">.</span><span class="n">try_from</span><span class="p">(</span><span class="n">np_weight_type</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">DataType</span><span class="p">)</span>
        <span class="n">torch_dtype</span> <span class="o">=</span> <span class="n">dtype</span><span class="o">.</span><span class="n">try_from</span><span class="p">(</span><span class="n">np_weight_type</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>

        <span class="k">if</span> <span class="n">sd_weight_name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">state_dict</span><span class="p">:</span>
            <span class="c1"># If weights is not in sd, we can leave it unchanged</span>
            <span class="k">continue</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">engine_weight_map</span><span class="p">[</span><span class="n">engine_weight_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">state_dict</span><span class="p">[</span><span class="n">sd_weight_name</span><span class="p">]</span>

        <span class="n">engine_weight_map</span><span class="p">[</span><span class="n">engine_weight_name</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
            <span class="n">engine_weight_map</span><span class="p">[</span><span class="n">engine_weight_name</span><span class="p">]</span>
            <span class="o">.</span><span class="n">clone</span><span class="p">()</span>
            <span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
            <span class="o">.</span><span class="n">contiguous</span><span class="p">()</span>
            <span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch_dtype</span><span class="p">),</span>
            <span class="n">trt_dtype</span><span class="p">,</span>
        <span class="p">)</span>

    <span class="k">return</span> <span class="n">engine_weight_map</span>


<span class="k">def</span><span class="w"> </span><span class="nf">_refit_single_trt_engine_with_gm</span><span class="p">(</span>
    <span class="n">new_gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span>
    <span class="n">old_engine</span><span class="p">:</span> <span class="n">trt</span><span class="o">.</span><span class="n">ICudaEngine</span><span class="p">,</span>
    <span class="n">input_list</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Any</span><span class="p">],</span>
    <span class="n">settings</span><span class="p">:</span> <span class="n">CompilationSettings</span> <span class="o">=</span> <span class="n">CompilationSettings</span><span class="p">(),</span>
    <span class="n">weight_name_map</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Refit a TensorRT Engine in place</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">with</span> <span class="n">unset_fake_temporarily</span><span class="p">():</span>
        <span class="n">refitted</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
        <span class="n">torch_device</span> <span class="o">=</span> <span class="n">get_model_device</span><span class="p">(</span><span class="n">new_gm</span><span class="p">)</span>
        <span class="n">refitter</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">Refitter</span><span class="p">(</span><span class="n">old_engine</span><span class="p">,</span> <span class="n">TRT_LOGGER</span><span class="p">)</span>
        <span class="n">weight_list</span> <span class="o">=</span> <span class="n">refitter</span><span class="o">.</span><span class="n">get_all_weights</span><span class="p">()</span>

        <span class="k">if</span> <span class="n">weight_name_map</span><span class="p">:</span>
            <span class="c1"># Get the refitting mapping</span>
            <span class="n">trt_wt_location</span> <span class="o">=</span> <span class="p">(</span>
                <span class="n">trt</span><span class="o">.</span><span class="n">TensorLocation</span><span class="o">.</span><span class="n">DEVICE</span>
                <span class="k">if</span> <span class="n">torch_device</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">&quot;cuda&quot;</span>
                <span class="k">else</span> <span class="n">trt</span><span class="o">.</span><span class="n">TensorLocation</span><span class="o">.</span><span class="n">HOST</span>
            <span class="p">)</span>

            <span class="n">constant_mapping</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="n">weight_name_map</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span>
                <span class="s2">&quot;constant_mapping&quot;</span><span class="p">,</span> <span class="p">{}</span>
            <span class="p">)</span>  <span class="c1"># type: ignore</span>
            <span class="n">mapping</span> <span class="o">=</span> <span class="n">construct_refit_mapping_from_weight_name_map</span><span class="p">(</span>
                <span class="n">weight_name_map</span><span class="p">,</span> <span class="n">new_gm</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span>
            <span class="p">)</span>
            <span class="n">constant_mapping_with_type</span> <span class="o">=</span> <span class="p">{}</span>

            <span class="k">for</span> <span class="n">constant_name</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">constant_mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
                <span class="n">np_weight_type</span> <span class="o">=</span> <span class="n">val</span><span class="o">.</span><span class="n">dtype</span>
                <span class="n">val_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">val</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
                <span class="n">trt_dtype</span> <span class="o">=</span> <span class="n">dtype</span><span class="o">.</span><span class="n">try_from</span><span class="p">(</span><span class="n">np_weight_type</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">DataType</span><span class="p">)</span>
                <span class="n">torch_dtype</span> <span class="o">=</span> <span class="n">dtype</span><span class="o">.</span><span class="n">try_from</span><span class="p">(</span><span class="n">np_weight_type</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
                <span class="n">constant_mapping_with_type</span><span class="p">[</span><span class="n">constant_name</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
                    <span class="n">val_tensor</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch_dtype</span><span class="p">),</span>
                    <span class="n">trt_dtype</span><span class="p">,</span>
                <span class="p">)</span>

            <span class="n">mapping</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">constant_mapping_with_type</span><span class="p">)</span>

            <span class="k">for</span> <span class="n">layer_name</span> <span class="ow">in</span> <span class="n">weight_list</span><span class="p">:</span>
                <span class="k">if</span> <span class="n">layer_name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">mapping</span><span class="p">:</span>
                    <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">layer_name</span><span class="si">}</span><span class="s2"> is not found in weight mapping.&quot;</span><span class="p">)</span>
                    <span class="k">continue</span>
                <span class="c1"># Use Numpy to create weights</span>
                <span class="n">weight</span><span class="p">,</span> <span class="n">weight_dtype</span> <span class="o">=</span> <span class="n">mapping</span><span class="p">[</span><span class="n">layer_name</span><span class="p">]</span>
                <span class="n">trt_wt_tensor</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">Weights</span><span class="p">(</span>
                    <span class="n">weight_dtype</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">(),</span> <span class="n">torch</span><span class="o">.</span><span class="n">numel</span><span class="p">(</span><span class="n">weight</span><span class="p">)</span>
                <span class="p">)</span>
                <span class="n">refitter</span><span class="o">.</span><span class="n">set_named_weights</span><span class="p">(</span><span class="n">layer_name</span><span class="p">,</span> <span class="n">trt_wt_tensor</span><span class="p">,</span> <span class="n">trt_wt_location</span><span class="p">)</span>
            <span class="k">assert</span> <span class="p">(</span>
                <span class="nb">len</span><span class="p">(</span><span class="n">refitter</span><span class="o">.</span><span class="n">get_missing_weights</span><span class="p">())</span> <span class="o">==</span> <span class="mi">0</span>
            <span class="p">),</span> <span class="s2">&quot;Fast refitting failed due to incomplete mapping&quot;</span>

        <span class="k">else</span><span class="p">:</span>
            <span class="n">mapping</span> <span class="o">=</span> <span class="n">construct_refit_mapping</span><span class="p">(</span><span class="n">new_gm</span><span class="p">,</span> <span class="n">input_list</span><span class="p">,</span> <span class="n">settings</span><span class="p">)</span>
            <span class="n">trt_wt_location</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">TensorLocation</span><span class="o">.</span><span class="n">HOST</span>
            <span class="k">for</span> <span class="n">layer_name</span> <span class="ow">in</span> <span class="n">weight_list</span><span class="p">:</span>
                <span class="k">if</span> <span class="n">layer_name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">mapping</span><span class="p">:</span>
                    <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">layer_name</span><span class="si">}</span><span class="s2"> is not found in weight mapping&quot;</span><span class="p">)</span>
                <span class="c1"># Use Numpy to create weights</span>
                <span class="n">weight</span><span class="p">,</span> <span class="n">datatype</span> <span class="o">=</span> <span class="n">mapping</span><span class="p">[</span><span class="n">layer_name</span><span class="p">]</span>
                <span class="n">trt_wt_tensor</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">Weights</span><span class="p">(</span><span class="n">datatype</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">ctypes</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">weight</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
                <span class="n">refitter</span><span class="o">.</span><span class="n">set_named_weights</span><span class="p">(</span><span class="n">layer_name</span><span class="p">,</span> <span class="n">trt_wt_tensor</span><span class="p">,</span> <span class="n">trt_wt_location</span><span class="p">)</span>
                <span class="n">refitted</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layer_name</span><span class="p">)</span>

            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">refitted</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">weight_list</span><span class="p">):</span>
                <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;Not all weights have been refitted!!!&quot;</span><span class="p">)</span>

        <span class="k">if</span> <span class="ow">not</span> <span class="n">refitter</span><span class="o">.</span><span class="n">refit_cuda_engine</span><span class="p">():</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="s2">&quot;Error: failed to refit new weights.&quot;</span><span class="p">)</span>
            <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span><span class="s2">&quot;Refitting failed.&quot;</span><span class="p">)</span>


<div class="viewcode-block" id="refit_module_weights"><a class="viewcode-back" href="../../../py_api/dynamo.html#torch_tensorrt.dynamo.refit_module_weights">[docs]</a><span class="k">def</span><span class="w"> </span><span class="nf">refit_module_weights</span><span class="p">(</span>
    <span class="n">compiled_module</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span> <span class="o">|</span> <span class="n">ExportedProgram</span><span class="p">,</span>
    <span class="n">new_weight_module</span><span class="p">:</span> <span class="n">ExportedProgram</span><span class="p">,</span>
    <span class="n">arg_inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="o">...</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">kwarg_inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">verify_output</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
    <span class="n">use_weight_map_cache</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
    <span class="n">in_place</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Refit a compiled graph module with ExportedProgram. This performs weight updates in compiled_module without recompiling the engine.</span>

<span class="sd">    Args:</span>
<span class="sd">        compiled_module: compiled TensorRT module that needs to be refitted.</span>
<span class="sd">                        This compiled_module should be compmiled by torch_tensorrt.dynamo.compile</span>
<span class="sd">                        or load it from disk using trt.load.</span>
<span class="sd">        new_weight_module: exported program with the updated weights. This one should have the same model architecture as the compiled module.</span>
<span class="sd">        arg_inputs: sample arg inputs. Optional, needed if output check</span>
<span class="sd">        kwarg_inputs: sample kwarg inputs. Optional, needed if output check</span>
<span class="sd">        verify_output: whether to verify output of refitted module</span>
<span class="sd">    Returns:</span>
<span class="sd">        A new compiled TensorRT module that has the updated weights.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">inline_module</span> <span class="o">=</span> <span class="kc">False</span>
    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">compiled_module</span><span class="p">,</span> <span class="n">ExportedProgram</span><span class="p">):</span>
        <span class="n">compiled_module</span> <span class="o">=</span> <span class="n">compiled_module</span><span class="o">.</span><span class="n">module</span><span class="p">()</span>

    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">compiled_module</span><span class="o">.</span><span class="n">named_children</span><span class="p">()))</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
        <span class="n">inline_module</span> <span class="o">=</span> <span class="kc">True</span>

    <span class="k">if</span> <span class="ow">not</span> <span class="n">in_place</span><span class="p">:</span>
        <span class="n">compiled_module</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">compiled_module</span><span class="p">)</span>
    <span class="k">elif</span> <span class="n">inline_module</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span>
            <span class="s2">&quot;Exported program does not support modifying in place. Please set in_place to false and use the returned graph module.&quot;</span>
        <span class="p">)</span>

    <span class="c1"># Get the settings and check the setting to be uniform</span>
    <span class="n">settings</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">CompilationSettings</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
    <span class="k">if</span> <span class="n">inline_module</span><span class="p">:</span>
        <span class="c1"># Obtain the settings</span>
        <span class="n">compiled_submodules</span> <span class="o">=</span> <span class="p">[</span>
            <span class="p">(</span><span class="n">name</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;_engine&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">),</span> <span class="n">engine</span><span class="p">)</span>
            <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">engine</span> <span class="ow">in</span> <span class="n">compiled_module</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
            <span class="k">if</span> <span class="s2">&quot;engine&quot;</span> <span class="ow">in</span> <span class="n">name</span>
        <span class="p">]</span>
        <span class="c1"># [(&#39;_run_on_acc_0&#39;, inline_module)]</span>
        <span class="n">encoded_metadata</span> <span class="o">=</span> <span class="n">compiled_submodules</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">__getstate__</span><span class="p">()[</span><span class="mi">0</span><span class="p">][</span>
            <span class="n">SERIALIZED_METADATA_IDX</span>
        <span class="p">]</span>
        <span class="k">assert</span> <span class="p">(</span>
            <span class="n">encoded_metadata</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span>
        <span class="p">),</span> <span class="s2">&quot;The engine provided is either not refittable or was built with a version of Torch-TensorRT that is too old, please recompile using the latest version&quot;</span>
        <span class="n">settings</span> <span class="o">=</span> <span class="n">TorchTensorRTModule</span><span class="o">.</span><span class="n">decode_metadata</span><span class="p">(</span><span class="n">encoded_metadata</span><span class="p">)[</span><span class="s2">&quot;settings&quot;</span><span class="p">]</span>
        <span class="c1"># Handle torch modules</span>
        <span class="n">compiled_submodules_map</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">compiled_submodules</span><span class="p">)</span>
        <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">submodule</span> <span class="ow">in</span> <span class="n">compiled_module</span><span class="o">.</span><span class="n">named_children</span><span class="p">():</span>
            <span class="n">compiled_submodules_map</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">submodule</span>

    <span class="k">else</span><span class="p">:</span>
        <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">submodule</span> <span class="ow">in</span> <span class="n">compiled_module</span><span class="o">.</span><span class="n">named_children</span><span class="p">():</span>
            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span>
                <span class="n">submodule</span><span class="p">,</span> <span class="p">(</span><span class="n">PythonTorchTensorRTModule</span><span class="p">,</span> <span class="n">TorchTensorRTModule</span><span class="p">)</span>
            <span class="p">):</span>
                <span class="k">continue</span>
            <span class="n">settings</span> <span class="o">=</span> <span class="n">submodule</span><span class="o">.</span><span class="n">settings</span>

    <span class="k">assert</span> <span class="n">settings</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>

    <span class="k">assert</span> <span class="p">(</span>
        <span class="ow">not</span> <span class="n">settings</span><span class="o">.</span><span class="n">immutable_weights</span>
    <span class="p">),</span> <span class="s2">&quot;Refitting is not enabled. Please recompile the engine with immutable_weights=False.&quot;</span>

    <span class="k">if</span> <span class="n">settings</span><span class="o">.</span><span class="n">debug</span><span class="p">:</span>
        <span class="n">set_log_level</span><span class="p">(</span><span class="n">logger</span><span class="o">.</span><span class="n">parent</span><span class="p">,</span> <span class="n">logging</span><span class="o">.</span><span class="n">DEBUG</span><span class="p">)</span>

    <span class="n">device</span> <span class="o">=</span> <span class="n">to_torch_tensorrt_device</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">arg_inputs</span><span class="p">:</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arg_inputs</span><span class="p">,</span> <span class="n">collections</span><span class="o">.</span><span class="n">abc</span><span class="o">.</span><span class="n">Sequence</span><span class="p">):</span>
            <span class="c1"># Prepare torch_trt inputs</span>
            <span class="n">arg_inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">arg_inputs</span><span class="p">]</span>
        <span class="n">torch_inputs</span> <span class="o">=</span> <span class="n">get_torch_inputs</span><span class="p">(</span><span class="n">arg_inputs</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>

    <span class="n">torch_kwarg_inputs</span><span class="p">:</span> <span class="n">Any</span> <span class="o">=</span> <span class="p">{}</span>
    <span class="k">if</span> <span class="n">kwarg_inputs</span><span class="p">:</span>
        <span class="n">torch_kwarg_inputs</span> <span class="o">=</span> <span class="n">get_torch_inputs</span><span class="p">(</span><span class="n">kwarg_inputs</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
    <span class="n">runtime</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">Runtime</span><span class="p">(</span><span class="n">TRT_LOGGER</span><span class="p">)</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">new_weight_module</span><span class="p">,</span> <span class="n">ExportedProgram</span><span class="p">):</span>
        <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;Input graph should be an ExportedProgram but got type </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">new_weight_module</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
        <span class="p">)</span>
    <span class="n">new_weight_module</span> <span class="o">=</span> <span class="n">pre_export_lowering</span><span class="p">(</span><span class="n">new_weight_module</span><span class="p">,</span> <span class="n">settings</span><span class="p">)</span>
    <span class="n">new_weight_module</span> <span class="o">=</span> <span class="n">new_weight_module</span><span class="o">.</span><span class="n">run_decompositions</span><span class="p">(</span>
        <span class="n">get_decompositions</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">enable_experimental_decompositions</span><span class="p">)</span>
    <span class="p">)</span>
    <span class="n">new_gm</span> <span class="o">=</span> <span class="n">new_weight_module</span><span class="o">.</span><span class="n">module</span><span class="p">()</span>
    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="s2">&quot;Input graph: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">new_gm</span><span class="o">.</span><span class="n">graph</span><span class="p">))</span>
    <span class="c1"># Apply lowering on the graph module</span>

    <span class="n">new_gm</span> <span class="o">=</span> <span class="n">post_lowering</span><span class="p">(</span><span class="n">new_gm</span><span class="p">,</span> <span class="n">settings</span><span class="p">)</span>

    <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Compilation Settings: </span><span class="si">%s</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">settings</span><span class="p">)</span>

    <span class="c1"># Set torch-executed ops</span>
    <span class="n">CONVERTERS</span><span class="o">.</span><span class="n">set_disallowed_targets</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">torch_executed_ops</span><span class="p">)</span>

    <span class="c1"># If specified, try using the fast partitioner and fall back to the global one on failure</span>
    <span class="k">if</span> <span class="n">settings</span><span class="o">.</span><span class="n">use_fast_partitioner</span><span class="p">:</span>
        <span class="k">try</span><span class="p">:</span>
            <span class="n">new_partitioned_module</span><span class="p">,</span> <span class="n">supported_ops</span> <span class="o">=</span> <span class="n">partitioning</span><span class="o">.</span><span class="n">fast_partition</span><span class="p">(</span>
                <span class="n">new_gm</span><span class="p">,</span>
                <span class="n">verbose</span><span class="o">=</span><span class="n">settings</span><span class="o">.</span><span class="n">debug</span><span class="p">,</span>
                <span class="n">min_block_size</span><span class="o">=</span><span class="n">settings</span><span class="o">.</span><span class="n">min_block_size</span><span class="p">,</span>
                <span class="n">torch_executed_ops</span><span class="o">=</span><span class="n">settings</span><span class="o">.</span><span class="n">torch_executed_ops</span><span class="p">,</span>
            <span class="p">)</span>
        <span class="k">except</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">passes</span><span class="o">.</span><span class="n">splitter_base</span><span class="o">.</span><span class="n">FxNetSplitterInternalError</span><span class="p">:</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="p">(</span>
                <span class="s2">&quot;Partitioning failed on the subgraph with fast partition. See trace above. &quot;</span>
                <span class="o">+</span> <span class="s2">&quot;Retrying with global partition.&quot;</span><span class="p">,</span>
                <span class="n">exc_info</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
            <span class="p">)</span>

            <span class="n">settings</span><span class="o">.</span><span class="n">use_fast_partitioner</span> <span class="o">=</span> <span class="kc">False</span>

    <span class="k">if</span> <span class="ow">not</span> <span class="n">settings</span><span class="o">.</span><span class="n">use_fast_partitioner</span><span class="p">:</span>
        <span class="n">new_partitioned_module</span><span class="p">,</span> <span class="n">supported_ops</span> <span class="o">=</span> <span class="n">partitioning</span><span class="o">.</span><span class="n">global_partition</span><span class="p">(</span>
            <span class="n">new_gm</span><span class="p">,</span>
            <span class="n">verbose</span><span class="o">=</span><span class="n">settings</span><span class="o">.</span><span class="n">debug</span><span class="p">,</span>
            <span class="n">min_block_size</span><span class="o">=</span><span class="n">settings</span><span class="o">.</span><span class="n">min_block_size</span><span class="p">,</span>
            <span class="n">torch_executed_ops</span><span class="o">=</span><span class="n">settings</span><span class="o">.</span><span class="n">torch_executed_ops</span><span class="p">,</span>
        <span class="p">)</span>

    <span class="k">if</span> <span class="n">inline_module</span><span class="p">:</span>
        <span class="c1"># Preprocess the partitioned module to be in the same format as the inline module</span>
        <span class="n">inline_torch_modules</span><span class="p">(</span><span class="n">new_partitioned_module</span><span class="p">)</span>
        <span class="n">new_partitioned_module</span><span class="o">.</span><span class="n">delete_all_unused_submodules</span><span class="p">()</span>
        <span class="c1"># Check the number of partitions and name</span>
        <span class="k">assert</span> <span class="p">{</span><span class="n">sm</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">sm</span> <span class="ow">in</span> <span class="n">new_partitioned_module</span><span class="o">.</span><span class="n">named_children</span><span class="p">()}</span> <span class="o">==</span> <span class="nb">set</span><span class="p">(</span>
            <span class="n">compiled_submodules_map</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
        <span class="p">),</span> <span class="s2">&quot;New weights module is not compatible with previously compiled Torch-TensorRT module&quot;</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">assert</span> <span class="p">{</span><span class="n">sm</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">sm</span> <span class="ow">in</span> <span class="n">new_partitioned_module</span><span class="o">.</span><span class="n">named_children</span><span class="p">()}</span> <span class="o">==</span> <span class="p">{</span>
            <span class="n">sm</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">sm</span> <span class="ow">in</span> <span class="n">compiled_module</span><span class="o">.</span><span class="n">named_children</span><span class="p">()</span>
        <span class="p">},</span> <span class="s2">&quot;New weights module is not compatible with previously compiled Torch-TensorRT module&quot;</span>
    <span class="c1"># 2. TODO: Check the hash of source fx.Graph and new fx.Graph</span>

    <span class="c1"># Iterate over all components that can be accelerated</span>
    <span class="c1"># Generate the corresponding TRT Module for those</span>

    <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">new_submodule</span> <span class="ow">in</span> <span class="n">new_partitioned_module</span><span class="o">.</span><span class="n">named_children</span><span class="p">():</span>
        <span class="c1"># Refit each submodule</span>
        <span class="c1"># Extract engine from the submodule</span>
        <span class="k">try</span><span class="p">:</span>
            <span class="k">if</span> <span class="n">inline_module</span><span class="p">:</span>
                <span class="n">weight_name_map</span> <span class="o">=</span> <span class="kc">None</span>
                <span class="n">compiled_submodule</span> <span class="o">=</span> <span class="n">compiled_submodules_map</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
                <span class="c1"># If this is a torch module, load the old state_dict</span>
                <span class="k">if</span> <span class="s2">&quot;_run_on_acc&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">name</span><span class="p">:</span>
                    <span class="n">compiled_submodule</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">new_submodule</span><span class="o">.</span><span class="n">state_dict</span><span class="p">())</span>
                    <span class="k">continue</span>
                <span class="k">else</span><span class="p">:</span>
                    <span class="n">engine_info</span> <span class="o">=</span> <span class="n">compiled_submodule</span><span class="o">.</span><span class="n">__getstate__</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
                    <span class="n">engine</span> <span class="o">=</span> <span class="n">get_engine_from_encoded_engine</span><span class="p">(</span>
                        <span class="n">engine_info</span><span class="p">[</span><span class="n">ENGINE_IDX</span><span class="p">],</span> <span class="n">runtime</span>
                    <span class="p">)</span>
                    <span class="k">if</span> <span class="n">use_weight_map_cache</span><span class="p">:</span>
                        <span class="n">encoded_metadata</span> <span class="o">=</span> <span class="n">compiled_submodule</span><span class="o">.</span><span class="n">__getstate__</span><span class="p">()[</span><span class="mi">0</span><span class="p">][</span>
                            <span class="n">SERIALIZED_METADATA_IDX</span>
                        <span class="p">]</span>
                        <span class="n">weight_name_map</span> <span class="o">=</span> <span class="n">TorchTensorRTModule</span><span class="o">.</span><span class="n">decode_metadata</span><span class="p">(</span>
                            <span class="n">encoded_metadata</span>
                        <span class="p">)[</span><span class="s2">&quot;weight_name_map&quot;</span><span class="p">]</span>
                        <span class="k">if</span> <span class="ow">not</span> <span class="n">weight_name_map</span><span class="p">:</span>
                            <span class="n">use_weight_map_cache</span> <span class="o">=</span> <span class="kc">False</span>
                            <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                                <span class="s2">&quot;This engine does not have a weight map cache. Rebuilding the weight map&quot;</span>
                            <span class="p">)</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">compiled_submodule</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">compiled_module</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>
                <span class="n">weight_name_map</span> <span class="o">=</span> <span class="kc">None</span>
                <span class="k">if</span> <span class="n">use_weight_map_cache</span><span class="p">:</span>
                    <span class="k">try</span><span class="p">:</span>
                        <span class="n">weight_name_map</span> <span class="o">=</span> <span class="n">compiled_submodule</span><span class="o">.</span><span class="n">weight_name_map</span>
                    <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
                        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span>
                            <span class="n">compiled_submodule</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">graph_module</span><span class="o">.</span><span class="n">GraphModule</span>
                        <span class="p">):</span>
                            <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                                <span class="s2">&quot;The module was compiled with an old version of Torch-TensorRT. Rebuilding the weight map.&quot;</span>
                            <span class="p">)</span>
                    <span class="k">if</span> <span class="ow">not</span> <span class="n">weight_name_map</span><span class="p">:</span>
                        <span class="n">use_weight_map_cache</span> <span class="o">=</span> <span class="kc">False</span>
                        <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                            <span class="s2">&quot;This engine does not have a weight map cache. Rebuilding the weight map&quot;</span>
                        <span class="p">)</span>
                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">compiled_submodule</span><span class="p">,</span> <span class="n">PythonTorchTensorRTModule</span><span class="p">):</span>
                    <span class="n">engine</span> <span class="o">=</span> <span class="n">compiled_submodule</span><span class="o">.</span><span class="n">engine</span>
                <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">compiled_submodule</span><span class="p">,</span> <span class="n">TorchTensorRTModule</span><span class="p">):</span>
                    <span class="n">engine_info</span> <span class="o">=</span> <span class="n">compiled_submodule</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">__getstate__</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
                    <span class="n">engine</span> <span class="o">=</span> <span class="n">get_engine_from_encoded_engine</span><span class="p">(</span>
                        <span class="n">engine_info</span><span class="p">[</span><span class="n">ENGINE_IDX</span><span class="p">],</span> <span class="n">runtime</span>
                    <span class="p">)</span>
                <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">compiled_submodule</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">graph_module</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">):</span>
                    <span class="c1"># This is graph break resulted by unsupported ops</span>
                    <span class="n">compiled_submodule</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">new_submodule</span><span class="o">.</span><span class="n">state_dict</span><span class="p">())</span>
                    <span class="k">continue</span>
                <span class="k">else</span><span class="p">:</span>
                    <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span>
                        <span class="s2">&quot;The type of graph module is not supported for refitting.&quot;</span>
                    <span class="p">)</span>
        <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span>
                <span class="s2">&quot;The type of graph module is not supported for refitting or two compiled modules do not match.&quot;</span>
            <span class="p">)</span>

        <span class="c1"># Get the submodule inputs for min, opt, max shapes of the graph inputs</span>
        <span class="n">submodule_inputs</span> <span class="o">=</span> <span class="n">partitioning</span><span class="o">.</span><span class="n">construct_submodule_inputs</span><span class="p">(</span><span class="n">new_submodule</span><span class="p">)</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span>
            <span class="s2">&quot;Refitting Submodule name: </span><span class="si">%s</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span>
            <span class="nb">str</span><span class="p">(</span><span class="n">name</span><span class="p">),</span>
        <span class="p">)</span>
        <span class="k">assert</span> <span class="n">submodule_inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
        <span class="c1"># Handle long/double inputs if requested by the user</span>
        <span class="k">if</span> <span class="n">settings</span><span class="o">.</span><span class="n">truncate_double</span><span class="p">:</span>
            <span class="n">submodule_inputs</span> <span class="o">=</span> <span class="n">repair_double_inputs</span><span class="p">(</span>
                <span class="n">new_partitioned_module</span><span class="p">,</span>
                <span class="n">new_submodule</span><span class="p">,</span>
                <span class="n">submodule_inputs</span><span class="p">,</span>
                <span class="n">to_torch_device</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">device</span><span class="p">),</span>
                <span class="n">name</span><span class="p">,</span>
            <span class="p">)</span>
        <span class="k">try</span><span class="p">:</span>
            <span class="n">_refit_single_trt_engine_with_gm</span><span class="p">(</span>
                <span class="n">new_gm</span><span class="o">=</span><span class="n">new_submodule</span><span class="p">,</span>
                <span class="n">old_engine</span><span class="o">=</span><span class="n">engine</span><span class="p">,</span>
                <span class="n">input_list</span><span class="o">=</span><span class="n">submodule_inputs</span><span class="p">,</span>
                <span class="n">settings</span><span class="o">=</span><span class="n">settings</span><span class="p">,</span>
                <span class="n">weight_name_map</span><span class="o">=</span><span class="n">weight_name_map</span><span class="p">,</span>
            <span class="p">)</span>

        <span class="k">except</span> <span class="ne">AssertionError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
            <span class="c1"># If fast_refit is used and failed, we fall back to regular refit</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
            <span class="k">if</span> <span class="n">use_weight_map_cache</span> <span class="ow">and</span> <span class="n">weight_name_map</span><span class="p">:</span>
                <span class="n">_refit_single_trt_engine_with_gm</span><span class="p">(</span>
                    <span class="n">new_gm</span><span class="o">=</span><span class="n">new_submodule</span><span class="p">,</span>
                    <span class="n">old_engine</span><span class="o">=</span><span class="n">engine</span><span class="p">,</span>
                    <span class="n">input_list</span><span class="o">=</span><span class="n">submodule_inputs</span><span class="p">,</span>
                    <span class="n">settings</span><span class="o">=</span><span class="n">settings</span><span class="p">,</span>
                    <span class="n">weight_name_map</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                <span class="p">)</span>

        <span class="c1"># clear EXCLUDE_WEIGHTS flag</span>
        <span class="n">serialization_config</span> <span class="o">=</span> <span class="n">engine</span><span class="o">.</span><span class="n">create_serialization_config</span><span class="p">()</span>
        <span class="n">serialization_config</span><span class="o">.</span><span class="n">clear_flag</span><span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">SerializationFlag</span><span class="o">.</span><span class="n">EXCLUDE_WEIGHTS</span><span class="p">)</span>
        <span class="n">serialized_engine</span> <span class="o">=</span> <span class="n">engine</span><span class="o">.</span><span class="n">serialize_with_config</span><span class="p">(</span><span class="n">serialization_config</span><span class="p">)</span>

        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span>
            <span class="n">compiled_submodule</span><span class="p">,</span> <span class="p">(</span><span class="n">PythonTorchTensorRTModule</span><span class="p">,</span> <span class="n">TorchTensorRTModule</span><span class="p">)</span>
        <span class="p">):</span>
            <span class="n">compiled_submodule</span><span class="o">.</span><span class="n">engine</span> <span class="o">=</span> <span class="kc">None</span>  <span class="c1"># Clear the engine for TorchTensorRTModule, otherwise it won&#39;t be updated</span>
            <span class="n">compiled_submodule</span><span class="o">.</span><span class="n">serialized_engine</span> <span class="o">=</span> <span class="nb">bytes</span><span class="p">(</span><span class="n">serialized_engine</span><span class="p">)</span>
            <span class="n">compiled_submodule</span><span class="o">.</span><span class="n">setup_engine</span><span class="p">()</span>

        <span class="k">elif</span> <span class="n">inline_module</span><span class="p">:</span>
            <span class="n">new_engine_info</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">engine_info</span><span class="p">)</span>
            <span class="n">new_engine_info</span><span class="p">[</span><span class="n">ENGINE_IDX</span><span class="p">]</span> <span class="o">=</span> <span class="nb">bytes</span><span class="p">(</span><span class="n">serialized_engine</span><span class="p">)</span>
            <span class="n">refitted_engine</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">Engine</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">new_engine_info</span><span class="p">))</span>
            <span class="nb">setattr</span><span class="p">(</span><span class="n">compiled_module</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">_engine&quot;</span><span class="p">,</span> <span class="n">refitted_engine</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">verify_output</span> <span class="ow">and</span> <span class="n">arg_inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">check_module_output</span><span class="p">(</span>
            <span class="n">new_module</span><span class="o">=</span><span class="n">new_gm</span><span class="p">,</span>
            <span class="n">refitted_module</span><span class="o">=</span><span class="n">compiled_module</span><span class="p">,</span>
            <span class="n">arg_inputs</span><span class="o">=</span><span class="n">torch_inputs</span><span class="p">,</span>
            <span class="n">kwarg_inputs</span><span class="o">=</span><span class="n">torch_kwarg_inputs</span><span class="p">,</span>
        <span class="p">):</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Refitting Succeed!&quot;</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">if</span> <span class="n">weight_name_map</span><span class="p">:</span>
                <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                    <span class="s2">&quot;Refitting with weight_name_map yielded incorrect result! The outputs do not match.&quot;</span>
                <span class="p">)</span>
                <span class="k">return</span> <span class="n">refit_module_weights</span><span class="p">(</span>
                    <span class="n">compiled_module</span><span class="p">,</span>
                    <span class="n">new_weight_module</span><span class="p">,</span>
                    <span class="n">arg_inputs</span><span class="p">,</span>
                    <span class="n">kwarg_inputs</span><span class="p">,</span>
                    <span class="n">verify_output</span><span class="p">,</span>
                    <span class="n">use_weight_map_cache</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                    <span class="n">in_place</span><span class="o">=</span><span class="n">in_place</span><span class="p">,</span>
                <span class="p">)</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="s2">&quot;Refitting Failed! The outputs do not match.&quot;</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Refitting Completed! Output verification skipped.&quot;</span><span class="p">)</span>

    <span class="k">return</span> <span class="n">compiled_module</span></div>


<span class="c1"># Util functions -----------</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">base64</span>


<span class="k">def</span><span class="w"> </span><span class="nf">get_engine_from_encoded_engine</span><span class="p">(</span>
    <span class="n">encoded_engine</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">runtime</span><span class="p">:</span> <span class="n">trt</span><span class="o">.</span><span class="n">Runtime</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">trt</span><span class="o">.</span><span class="n">ICudaEngine</span><span class="p">:</span>
    <span class="n">serialized_engine</span> <span class="o">=</span> <span class="n">base64</span><span class="o">.</span><span class="n">b64decode</span><span class="p">(</span><span class="n">encoded_engine</span><span class="p">)</span>
    <span class="n">engine</span> <span class="o">=</span> <span class="n">runtime</span><span class="o">.</span><span class="n">deserialize_cuda_engine</span><span class="p">(</span><span class="n">serialized_engine</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">engine</span>
</pre></div>

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